The present invention relates in general to systems used to determine whether the deployment of an airbag should be prevented or modified due to the proximity of the occupant to the airbag. In particular, the present invention is an image processing system that receives a stream of two-dimensional images and applies iterative and probability-weighted processes to infer three-dimensional characteristics to those images, and predict whether the upper torso of the occupant would be within the At-Risk-Zone (“ARZ”) by the time that an air bag would actually deploy.
Conventional airbag deployment systems have contributed significantly to the safety of occupants in automobile crashes. However, there may be occasions when due to the physical proximity of an occupant to an airbag, that the deployment of an airbag may not be desirable. Airbag disablement systems are known in the art. Some of those systems attempt to process the distance between the occupant and the airbag. However, there are several significant problems with that existing art.
First, such systems require highly expensive cameras. Timing is critical to any system used to determine whether or not an airbag should be deploy. A standard video camera operates at a frequency between 50-100 hz and captures between 50 to 100 image frames per second of operation. Effective airbag determinations require more frequent updates, of approximately 200 updates per second (200 hz). Moreover, it would be desirable for an image processing system to predict the occupant's position in advance instead of merely identifying the occupant's location at the time that the image is captured. It would also be desirable if accurate predictions could be generated at a faster rate than the camera speed so that a less expansive standard video camera could be used instead of a more expensive highly specialized high-speed camera.
Second, prior art systems are highly susceptible to “noise” because prior art systems focus solely on the most recent image, and ignore the series of images captured mere fractions of a second earlier. “Noise” results from several factors, including the inherent imperfections of the segmentation process which is the process of extracting a segmented image, an image of the occupant, in isolation from the surrounding area, from the ambient image, an image of the occupant and an image of the surrounding area. It would be desirable for an image processing system to utilize an iterative process that would integrate the information contained in the most recent image into a comprehensive framework that includes prior predictions and indirectly, the prior images used to make those prior predictions. It would also be desirable for all predictions to be weighted predictions. Moreover, it would be helpful for such weighted predictions to include probabilities associated with predefined occupant states such as leaning left towards the driver, leaning right away from the driver, or sitting upright, and predefined occupant modes such as crash, stationary, or human.
The third problem with prior art systems is that they rely on two-dimensional images. The images captured by cameras, including video cameras, are inherently two dimensional images. It would be useful if three-dimensional information could be inferred from a series of two dimensional images. Moreover, it would be helpful if predefined occupant states were incorporated into the iterative process of deriving a three-dimensional information from a series of two-dimensional images.